Pro forma modeling of cryptocurrency returns, volatilities, linkages and portfolio characteristics

Purpose Critics say cryptocurrencies are hard to predict and lack both economic value accounting standards, while supporters argue they revolutionary financial technology a new asset class. This study aims help modelers compare with other classes (such as gold, stocks bond markets) develop cryptocurrency forecast models. Design/methodology/approach Daily data from 12/31/2013 08/01/2020 (including the COVID-19 pandemic period) for top six that constitute 80% of market used. Cryptocurrency price, return volatility forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect (REM), panel vector error correction (VECM) generalized autoregressive conditional heteroskedasticity (GARCH). Fama French's five-factor analysis, frequently used method stock returns, is conducted on returns in panel-data setting. Finally, an efficient frontier produced without see how adding portfolio makes difference. Findings The seven findings this analysis summarized follows: (1) VECM produces best out-of-sample price prices; (2) unlike cash purposes very volatile: standard deviations daily several times larger than those assets; (3) not substitute gold safe-haven asset; (4) most significant determinants emerging markets index, S&P 500 index (VIX); (5) their persistent can be GARCH model; (6) setting, exhibit negative alpha, high beta, similar small growth (7) offers more choices investors resembles levered portfolio. Practical implications One tasks econometrics profession building pro forma models meet standards satisfy auditors. paper undertook such activity by deploying methods applying them Originality/value attempts contribute existing academic literature three ways: Pro forecasting: established techniques (as opposed novel methods) deployed group: instead analyzing one currency at time running risk missing out cross-sectional effects done researchers), top-six market, analyzed together group methods; Cryptocurrencies assets portfolio: To understand linkages between characteristics, investment